SoroushVahidi/scidocs huggingface dataset:
Forensic mycology: current perspectives
(2015)

(unported, v3.0) License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress.com/permissions.php Research and Reports in Forensic Medical Science 2015:5 75–83 Research and Reports in Forensic Medical Science Dovepress
Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception
(2013)

This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.
THE RELATIONSHIPS BETWEEN IT FLEXIBILITY, IT-BUSINESS STRATEGIC ALIGNMENT, AND IT CAPABILITY
(2011)

What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.
Deep Blind Image Inpainting
(2017)

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.
Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz
(2016)

The Yiddish language is over 1,000 years old and incorporates German, Slavic, and Hebrew elements. The prevalent view claims Yiddish has a German origin, whereas the opposing view posits a Slavic origin with strong Iranian and weak Turkic substrata. One of the major difficulties in deciding between these hypotheses is the unknown geographical origin of Yiddish speaking Ashkenazic Jews (AJs). An analysis of 393 Ashkenazic, Iranian, and mountain Jews and over 600 non-Jewish genomes demonstrated that Greeks, Romans, Iranians, and Turks exhibit the highest genetic similarity with AJs. The Geographic Population Structure analysis localized most AJs along major primeval trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from "Ashkenaz." Iranian and mountain Jews were localized along trade routes on the Turkey's eastern border. Loss of maternal haplogroups was evident in non-Yiddish speaking AJs. Our results suggest that AJs originated from a Slavo-Iranian confederation, which the Jews call "Ashkenazic" (i.e., "Scythian"), though these Jews probably spoke Persian and/or Ossete. This is compatible with linguistic evidence suggesting that Yiddish is a Slavic language created by Irano-Turko-Slavic Jewish merchants along the Silk Roads as a cryptic trade language, spoken only by its originators to gain an advantage in trade. Later, in the 9th century, Yiddish underwent relexification by adopting a new vocabulary that consists of a minority of German and Hebrew and a majority of newly coined Germanoid and Hebroid elements that replaced most of the original Eastern Slavic and Sorbian vocabularies, while keeping the original grammars intact.
A 6.25Gbps Feed-forward Equalizer in 0.18μm CMOS Technology for SerDes
(2012)

This paper presents a 6.25Gbps feed-forward equalizer (FFE) to reduce the inter-symbol-interference (ISI) in high-speed transmission backplane. The 3-tap fractionally spaced equalizer consists of a delay line, multiplier & summer cells and an output circuit. Active-inductive peaking circuit and capacitor-degenerated circuit are used in the delay line and the output stage respectively to meet the bandwidth demand. The proposed FFE has been implemented in TSMC 0.18μm CMOS technology with a whole area of 0.5mm×0.51mm including I/O pads. The power consumption of the core circuit is 31.7mW under 1.8V power supply. Post simulation results show that the distorted signal through a 24-inch backplane is well recovered by this equalizer.
Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach
(2015)

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.
Orthogonal Coaxial Cavity Filters With Distributed Cross-Coupling
(2011)

In this letter, six-pole filters are presented, in which the coaxial cavity resonators are arranged with 90 degree rotated spatial orientation. Cross-coupling between nonadjacent resonators will appear. In the new proposed filter topologies the cross-coupling elements are not arranged in separated triplet or quadruplet blocks. They interlace to form distributed cross-coupling. Synthesis of the distributed coupling matrix based on the symmetric canonical prototype is discussed. Two different bandpass filter topologies are presented. The first one has an inline arrangement of six orthogonal coaxial cavity resonators. Its distributed cross-coupling consists of four interlaced triplets, which generate two transmission zeros above the passband. The second filter design has a folded arrangement of two inline triplets with strong cross-coupling. An additional interlaced quadruplet pushes the resulting transmission zeros to the complex frequency domain. Measurement results of two corresponding filter prototypes confirm the predicted performance.
A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: results of a pilot study.
(2018)

OBJECTIVE
Informal caregivers often experience psychological distress due to the changing functioning of the person with dementia they care for. Improved understanding of the person with dementia reduces psychological distress. To enhance understanding and empathy in caregivers, an innovative technology virtual reality intervention Through the D'mentia Lens (TDL) was developed to experience dementia, consisting of a virtual reality simulation movie and e-course. A pilot study of TDL was conducted.


METHODS
A pre-test-post-test design was used. Informal caregivers filled out questionnaires assessing person-centeredness, empathy, perceived pressure from informal care, perceived competence and quality of the relationship. At post-test, additional questions about TDL's feasibility were asked.


RESULTS
Thirty-five caregivers completed the pre-test and post-test. Most participants were satisfied with TDL and stated that TDL gave more insight in the perception of the person with dementia. The simulation movie was graded 8.03 out of 10 and the e-course 7.66. Participants significantly improved in empathy, confidence in caring for the person with dementia, and positive interactions with the person with dementia.


CONCLUSION
TDL is feasible for informal caregivers and seems to lead to understanding of and insight in the experience of people with dementia. Therefore, TDL could support informal caregivers in their caregiving role.
Substrate Integrated Waveguide ( SIW ) to Microstrip Transition at X-Band
(2014)

Substrate integrated waveguide (SIW) is a new form of transmission line. It facilitates the realization of nonplanar (waveguide based) circuits into planar form for easy integration with other planar (microstrip) circuits and systems. This paper describes the design of an SIW to microstrip transition. The transition is broadband covering the frequency range of 8 – 12GHz. The measured in-band insertion loss is below 0.6dB while the return loss is less than 10dB. The circuit is simulated in HFSS and results are measured on vector network analyzer (VNA). Keywords— Microstrip; Substrate Integrated Waveguide; Transition
Why experts make errors
(2006)

Expert latent f ingerprint examiners were presented with fingerprints taken from real criminal cases. Half of the prints had been previously judged as individualizations and the other half as exclusions. We re-presented the same prints to the same experts who had judged them previously, but provided biasing contextual information in both the individualizations and exclusions. A control set of individualizations and exclusions was also re-presented as part of the study. The control set had no biasing contextual information associated with it. Each expert examined a total of eight past decisions. Two-thirds of the experts made inconsistent decisions. The f indings are discussed in terms of psychological and cognitive vulnerabilities.
Completed Local Binary Count for Rotation Invariant Texture Classification
(2012)

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.
Description of interest regions with local binary patterns
(2009)

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.
Survey on LBP based texture descriptors for image classification
(2012)

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. 2011 Elsevier Ltd. All rights reserved.
The Language of Place: Semantic Value from Geospatial Context
(2017)

There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.
Spherical CNNs
(2018)

Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.
A novel method for providing relational databases with rich semantics and natural language processing
(2017)

Purpose – With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it doesn’t offer any semantic support for the stored data which can facilitate data access for users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDB’s is not wide-spread due to their deficiencies in understanding natural language queries. In this sense, this paper proposes a novel method for intelligent understanding of natural language queries using semantically enriched database sources. Design/Methodology/Approach – First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing natural language queries uses the stored semantics to generate a semantic-tree. Findings – The evaluation part of our work show the advantages of using a semantically enriched database source to understand natural language queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a natural language query. Originality/value – Existing NLIDB’s aren’t yet a standard option for interfacing a relational database due their lack for understanding natural language queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying natural language elements by their semantic nature in order to generate a semantic-tree. This last is a key solution toward intelligent understanding of natural language queries to relational databases.
Teaching a lay theory before college narrows achievement gaps at scale.
(2016)

Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.
Experts ’ Views on Digital Parenting Strategies
(2017)

American teenagers are spending much of their time online. Online communities offer teens perspective, acceptance, connection, and education that were once more limited by geography. However, the online world also opens teens to risks. Adults must decide how to guide teen online behavior in a space that they may not understand. We interviewed 16 experts about teen online behavior, related benefits and risks, and risk mitigation methods. We found that experts agree on certain mitigation methods, especially communication and education about online behavior, but are divided over monitoring. We propose a set of possible solutions that promote online safety while preserving teens’ privacy.
Using Machine Learning Methods and Linguistic Features in Single-Document Extractive Summarization
(2016)

Extractive summarization of text documents usually consists of ranking the document sentences and extracting the top-ranked sentences subject to the summary length constraints. In this paper, we explore the contribution of various supervised learning algorithms to the sentence ranking task. For this purpose, we introduce a novel sentence ranking methodology based on the similarity score between a candidate sentence and benchmark summaries. Our experiments are performed on three benchmark summarization corpora: DUC-2002, DUC2007 and MultiLing-2013. The popular linear regression model achieved the best results in all evaluated datasets. Additionally, the linear regression model, which included POS (Part-of-Speech)-based features, outperformed the one with statistical features only.